


import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
A			:						Optional[int]        =  logging.get_logger(__name__)






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						ASTConfig()

		if "10-10" in model_name:
				pass
		elif "speech-commands" in model_name:
				lowercase__	     =						128
		elif "12-12" in model_name:
				lowercase__	     =						12
				lowercase__	     =						12
		elif "14-14" in model_name:
				lowercase__	     =						14
				lowercase__	     =						14
		elif "16-16" in model_name:
				lowercase__	     =						16
				lowercase__	     =						16
		else:
				raise ValueError("""Model not supported"""	)

		lowercase__	     =						"""huggingface/label-files"""
		if "speech-commands" in model_name:
				lowercase__	     =						35
				lowercase__	     =						"""speech-commands-v2-id2label.json"""
		else:
				lowercase__	     =						527
				lowercase__	     =						"""audioset-id2label.json"""

		lowercase__	     =						json.load(open(hf_hub_download(__magic_name__       ,       __magic_name__       ,       repo_type="""dataset"""	)       ,       """r"""	)	)
		lowercase__	     =						{int(__magic_name__	): v for k, v in idalabel.items()}
		lowercase__	     =						idalabel
		lowercase__	     =						{v: k for k, v in idalabel.items()}

		return config






def 					UpperCamelCase   (      __magic_name__	:				List[str]	)  ->    Dict:
		"""simple docstring"""




		if "module.v" in name:
				lowercase__	     =						name.replace("""module.v"""       ,       """audio_spectrogram_transformer"""	)
		if "cls_token" in name:
				lowercase__	     =						name.replace("""cls_token"""       ,       """embeddings.cls_token"""	)
		if "dist_token" in name:
				lowercase__	     =						name.replace("""dist_token"""       ,       """embeddings.distillation_token"""	)
		if "pos_embed" in name:
				lowercase__	     =						name.replace("""pos_embed"""       ,       """embeddings.position_embeddings"""	)
		if "patch_embed.proj" in name:
				lowercase__	     =						name.replace("""patch_embed.proj"""       ,       """embeddings.patch_embeddings.projection"""	)
		# transformer blocks
		if "blocks" in name:
				lowercase__	     =						name.replace("""blocks"""       ,       """encoder.layer"""	)
		if "attn.proj" in name:
				lowercase__	     =						name.replace("""attn.proj"""       ,       """attention.output.dense"""	)
		if "attn" in name:
				lowercase__	     =						name.replace("""attn"""       ,       """attention.self"""	)
		if "norm1" in name:
				lowercase__	     =						name.replace("""norm1"""       ,       """layernorm_before"""	)
		if "norm2" in name:
				lowercase__	     =						name.replace("""norm2"""       ,       """layernorm_after"""	)
		if "mlp.fc1" in name:
				lowercase__	     =						name.replace("""mlp.fc1"""       ,       """intermediate.dense"""	)
		if "mlp.fc2" in name:
				lowercase__	     =						name.replace("""mlp.fc2"""       ,       """output.dense"""	)
		# final layernorm
		if "audio_spectrogram_transformer.norm" in name:
				lowercase__	     =						name.replace("""audio_spectrogram_transformer.norm"""       ,       """audio_spectrogram_transformer.layernorm"""	)
		# classifier head
		if "module.mlp_head.0" in name:
				lowercase__	     =						name.replace("""module.mlp_head.0"""       ,       """classifier.layernorm"""	)
		if "module.mlp_head.1" in name:
				lowercase__	     =						name.replace("""module.mlp_head.1"""       ,       """classifier.dense"""	)

		return name






def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				Any	)  ->    int:
		"""simple docstring"""




		for key in orig_state_dict.copy().keys():
				lowercase__	     =						orig_state_dict.pop(__magic_name__	)

				if "qkv" in key:
						lowercase__	     =						key.split("""."""	)
						lowercase__	     =						int(key_split[3]	)
						lowercase__	     =						config.hidden_size
						if "weight" in key:
								lowercase__	     =						val[:dim, :]
								lowercase__	     =						val[dim : dim * 2, :]
								lowercase__	     =						val[-dim:, :]
						else:
								lowercase__	     =						val[:dim]
								lowercase__	     =						val[dim : dim * 2]
								lowercase__	     =						val[-dim:]
				else:
						lowercase__	     =						val

		return orig_state_dict






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """module.v.head.weight""",
		    """module.v.head.bias""",
		    """module.v.head_dist.weight""",
		    """module.v.head_dist.bias""",
		]
		for k in ignore_keys:
				state_dict.pop(__magic_name__       ,       __magic_name__	)






@torch.no_grad()
def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				Any       ,       __magic_name__	:				Optional[Any]=False	)  ->    List[str]:
		"""simple docstring"""




		lowercase__	     =						get_audio_spectrogram_transformer_config(__magic_name__	)

		lowercase__	     =						{
		    """ast-finetuned-audioset-10-10-0.4593""": (
		        """https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
		    ),
		    """ast-finetuned-audioset-10-10-0.450""": (
		        """https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
		    ),
		    """ast-finetuned-audioset-10-10-0.448""": (
		        """https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
		    ),
		    """ast-finetuned-audioset-10-10-0.448-v2""": (
		        """https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
		    ),
		    """ast-finetuned-audioset-12-12-0.447""": (
		        """https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
		    ),
		    """ast-finetuned-audioset-14-14-0.443""": (
		        """https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
		    ),
		    """ast-finetuned-audioset-16-16-0.442""": (
		        """https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
		    ),
		    """ast-finetuned-speech-commands-v2""": (
		        """https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
		    ),
		}

		# load original state_dict
		lowercase__	     =						model_name_to_url[model_name]
		lowercase__	     =						torch.hub.load_state_dict_from_url(__magic_name__       ,       map_location="""cpu"""	)
		# remove some keys
		remove_keys(__magic_name__	)
		# rename some keys
		lowercase__	     =						convert_state_dict(__magic_name__       ,       __magic_name__	)

		# load 🤗 model
		lowercase__	     =						ASTForAudioClassification(__magic_name__	)
		model.eval()

		model.load_state_dict(__magic_name__	)

		# verify outputs on dummy input
		# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
		lowercase__	     =						-4.2_6_7_7_3_9_3 if """speech-commands""" not in model_name else -6.8_4_5_9_7_8
		lowercase__	     =						4.5_6_8_9_9_7_4 if """speech-commands""" not in model_name else 5.5_6_5_4_5_2_6
		lowercase__	     =						1024 if """speech-commands""" not in model_name else 128
		lowercase__	     =						ASTFeatureExtractor(mean=__magic_name__       ,       std=__magic_name__       ,       max_length=__magic_name__	)

		if "speech-commands" in model_name:
				lowercase__	     =						load_dataset("""speech_commands"""       ,       """v0.02"""       ,       split="""validation"""	)
				lowercase__	     =						dataset[0]["""audio"""]["""array"""]
		else:
				lowercase__	     =						hf_hub_download(
				    repo_id="""nielsr/audio-spectogram-transformer-checkpoint"""       ,       filename="""sample_audio.flac"""       ,       repo_type="""dataset"""       ,       )

				lowercase__     ,      lowercase__	     =						torchaudio.load(__magic_name__	)
				lowercase__	     =						waveform.squeeze().numpy()

		lowercase__	     =						feature_extractor(__magic_name__       ,       sampling_rate=1_6000       ,       return_tensors="""pt"""	)

		# forward pass
		lowercase__	     =						model(**__magic_name__	)
		lowercase__	     =						outputs.logits

		if model_name == "ast-finetuned-audioset-10-10-0.4593":
				lowercase__	     =						torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]	)
		elif model_name == "ast-finetuned-audioset-10-10-0.450":
				lowercase__	     =						torch.tensor([-1.1_9_8_6, -7.0_9_0_3, -8.2_7_1_8]	)
		elif model_name == "ast-finetuned-audioset-10-10-0.448":
				lowercase__	     =						torch.tensor([-2.6_1_2_8, -8.0_0_8_0, -9.4_3_4_4]	)
		elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
				lowercase__	     =						torch.tensor([-1.5_0_8_0, -7.4_5_3_4, -8.8_9_1_7]	)
		elif model_name == "ast-finetuned-audioset-12-12-0.447":
				lowercase__	     =						torch.tensor([-0.5_0_5_0, -6.5_8_3_3, -8.0_8_4_3]	)
		elif model_name == "ast-finetuned-audioset-14-14-0.443":
				lowercase__	     =						torch.tensor([-0.3_8_2_6, -7.0_3_3_6, -8.2_4_1_3]	)
		elif model_name == "ast-finetuned-audioset-16-16-0.442":
				lowercase__	     =						torch.tensor([-1.2_1_1_3, -6.9_1_0_1, -8.3_4_7_0]	)
		elif model_name == "ast-finetuned-speech-commands-v2":
				lowercase__	     =						torch.tensor([6.1_5_8_9, -8.0_5_6_6, -8.7_9_8_4]	)
		else:
				raise ValueError("""Unknown model name"""	)
		if not torch.allclose(logits[0, :3]       ,       __magic_name__       ,       atol=1E-4	):
				raise ValueError("""Logits don't match"""	)
		print("""Looks ok!"""	)

		if pytorch_dump_folder_path is not None:
				Path(__magic_name__	).mkdir(exist_ok=__magic_name__	)
				print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
				model.save_pretrained(__magic_name__	)
				print(f'''Saving feature extractor to {pytorch_dump_folder_path}'''	)
				feature_extractor.save_pretrained(__magic_name__	)

		if push_to_hub:
				print("""Pushing model and feature extractor to the hub..."""	)
				model.push_to_hub(f'''MIT/{model_name}'''	)
				feature_extractor.push_to_hub(f'''MIT/{model_name}'''	)


if __name__ == "__main__":
	A			:						Optional[Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='ast-finetuned-audioset-10-10-0.4593',
	    type=str,
	    help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	A			:						List[Any]        =  parser.parse_args()
	convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 							a__:
			a_	: CommonSchedulerState

			# setable values
			a_	: jnp.ndarray
			a_	: jnp.ndarray
			a_	: Optional[int]									=      None






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					return cls(common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase						)





@dataclass
class 							a__( snake_case__     ):
			a_	: DDPMSchedulerState





class 							a__( snake_case__   ,      snake_case__     ):
			a_	: Union[str, Any]									=      [e.name for e in FlaxKarrasDiffusionSchedulers]

			a_	: jnp.dtype






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return True






			@register_to_config
			def __init__(     self					,   _UpperCAmelCase = 1000					,   _UpperCAmelCase = 0.0_001					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = "linear"					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "fixed_small"					,   _UpperCAmelCase = True					,   _UpperCAmelCase = "epsilon"					,   _UpperCAmelCase = jnp.floataa					,   )							->							Union[str, Any]:
					snake_case__	     =dtype






			def 						_lowercase  (     self					,   _UpperCAmelCase = None						)							->							DDPMSchedulerState:
					if common is None:
							snake_case__	     =CommonSchedulerState.create(self						)

					# standard deviation of the initial noise distribution
					snake_case__	     =jnp.array(1.0					,   dtype=self.dtype						)

					snake_case__	     =jnp.arange(0					,   self.config.num_train_timesteps						).round()[::-1]

					return DDPMSchedulerState.create(
					    common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							jnp.ndarray:
					return sample






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = ()						)							->							DDPMSchedulerState:
					snake_case__	     =self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					snake_case__	     =(jnp.arange(0					,   _UpperCAmelCase						) * step_ratio).round()[::-1]

					return state.replace(
					    num_inference_steps=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							Optional[Any]:
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					snake_case__	     =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

					if variance_type is None:
							snake_case__	     =self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small":
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)
					# for rl-diffuser https://arxiv.org/abs/2205.09991
					elif variance_type == "fixed_small_log":
							snake_case__	     =jnp.log(jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)						)
					elif variance_type == "fixed_large":
							snake_case__	     =state.common.betas[t]
					elif variance_type == "fixed_large_log":
							# Glide max_log
							snake_case__	     =jnp.log(state.common.betas[t]						)
					elif variance_type == "learned":
							return predicted_variance
					elif variance_type == "learned_range":
							snake_case__	     =variance
							snake_case__	     =state.common.betas[t]
							snake_case__	     =(predicted_variance + 1) / 2
							snake_case__	     =frac * max_log + (1 - frac) * min_log

					return variance






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   )							->							Union[FlaxDDPMSchedulerOutput, Tuple]:
					snake_case__	     =timestep

					if key is None:
							snake_case__	     =jax.random.PRNGKey(0						)

					if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							snake_case__     ,	snake_case__	     =jnp.split(_UpperCAmelCase					,   sample.shape[1]					,   axis=1						)
					else:
							snake_case__	     =None

					# 1. compute alphas, betas
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)
					snake_case__	     =1 - alpha_prod_t
					snake_case__	     =1 - alpha_prod_t_prev

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
							snake_case__	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
							snake_case__	     =model_output
					elif self.config.prediction_type == "v_prediction":
							snake_case__	     =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
					else:
							raise ValueError(
							    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
							    ' for the FlaxDDPMScheduler.'						)

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   -1					,   1						)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
					snake_case__	     =state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					def random_variance():
							snake_case__	     =jax.random.split(_UpperCAmelCase					,   num=1						)
							snake_case__	     =jax.random.normal(_UpperCAmelCase					,   shape=model_output.shape					,   dtype=self.dtype						)
							return (self._get_variance(_UpperCAmelCase					,   _UpperCAmelCase					,   predicted_variance=_UpperCAmelCase						) ** 0.5) * noise

					snake_case__	     =jnp.where(t > 0					,   random_variance()					,   jnp.zeros(model_output.shape					,   dtype=self.dtype						)						)

					snake_case__	     =pred_prev_sample + variance

					if not return_dict:
							return (pred_prev_sample, state)

					return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase					,   state=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return add_noise_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return get_velocity_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def __len__(     self						)							->							Optional[int]:
					return self.config.num_train_timesteps




